AI Agents Specialization¶

🤖 Overview¶

Build autonomous AI systems that can reason, use tools, and collaborate to solve complex tasks!

Time: 3-4 weeks | 60-80 hours
Prerequisites:

✅ LLMs & Prompt Engineering (Phase 10)
✅ RAG systems (Phase 7)
✅ Python programming

Outcome: Deploy production-ready AI agent systems with tool use and multi-agent collaboration

🗂️ Module Structure¶

ai-agents/
├── 00_START_HERE.ipynb                # Agent overview & quick demo
├── 01_function_calling.ipynb          # Tool use basics
├── 02_react_pattern.ipynb             # ReAct reasoning loop
├── 03_langgraph_agents.ipynb          # LangGraph framework
├── 04_microsoft_agents.ipynb          # Microsoft Agent Framework
├── 05_autogen_multiagent.ipynb        # Multi-agent systems
├── 06_crewai.ipynb                    # Role-based collaboration
├── 07_memory_state.ipynb              # Long-term memory
├── 08_evaluation.ipynb                # Testing agents
├── 09_production.ipynb                # Deployment patterns
├── projects/
│   ├── research_assistant/            # Web research agent
│   ├── code_reviewer/                 # GitHub PR agent
│   ├── data_analyst/                  # SQL + Python agent
│   ├── customer_support/              # Multi-turn support
│   └── workflow_automation/           # Complex workflows
├── tools/
│   ├── web_search.py
│   ├── code_execution.py
│   ├── database_query.py
│   └── file_operations.py
└── README.md

🚀 Quick Start¶

Example: Simple ReAct Agent¶

from openai import OpenAI
import json

client = OpenAI()

tools = [
    {
        "type": "function",
        "function": {
            "name": "web_search",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    }
]

def web_search(query):
    """Simulate web search."""
    return f"Results for '{query}': [Sample results...]"

# Agent loop
messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]

while True:
    response = client.chat.completions.create(
        model="gpt-4",
        messages=messages,
        tools=tools
    )
    
    message = response.choices[0].message
    
    # Check if agent wants to use a tool
    if message.tool_calls:
        # Execute tool
        for tool_call in message.tool_calls:
            function_name = tool_call.function.name
            function_args = json.loads(tool_call.function.arguments)
            
            if function_name == "web_search":
                result = web_search(**function_args)
                
                # Add tool result to conversation
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": result
                })
    else:
        # Agent is done
        print(message.content)
        break

Example: Multi-Agent System with AutoGen¶

from autogen import AssistantAgent, UserProxyAgent

# Define agents with different roles
researcher = AssistantAgent(
    name="Researcher",
    system_message="You research topics thoroughly and provide facts.",
    llm_config={"model": "gpt-4"}
)

writer = AssistantAgent(
    name="Writer",
    system_message="You write clear, engaging content based on research.",
    llm_config={"model": "gpt-4"}
)

critic = AssistantAgent(
    name="Critic",
    system_message="You review and provide constructive feedback.",
    llm_config={"model": "gpt-4"}
)

user = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=3
)

# Agents collaborate
user.initiate_chat(
    researcher,
    message="Write an article about quantum computing"
)

🛠️ Technologies You’ll Use¶

Agent Frameworks:

LangGraph (LangChain)
Microsoft Agent Framework
AutoGen (Microsoft)
CrewAI
Semantic Kernel

LLM Providers:

OpenAI (function calling, Assistants API)
Anthropic (Claude with tools)
Local models (via Ollama)

Tools & Integrations:

Web search (Serper, Tavily)
Code execution (E2B, Docker)
Database queries (SQL)
File operations
API calls

Orchestration:

LangSmith (monitoring)
LangServe (deployment)
Temporal (workflows)

📊 Key Concepts Explained¶

Agent vs. Chatbot¶

Chatbot:
User → LLM → Response

Agent:
User → Agent
       ↓
    Reasoning (What do I need?)
       ↓
    Action (Use tools)
       ↓
    Observation (Results)
       ↓
    Repeat until done
       ↓
    Final Answer

ReAct Pattern¶

Thought: I need to find the weather
Action: web_search("Tokyo weather")
Observation: Tokyo is 15°C, sunny
Thought: I have the information
Answer: The weather in Tokyo is 15°C and sunny

Multi-Agent Collaboration¶

User Request
    ↓
Supervisor Agent (coordinates)
    ↓
├─ Researcher (gathers info)
├─ Analyst (processes data)
├─ Writer (creates content)
└─ Critic (reviews)
    ↓
Final Output

🎯 Projects¶

1. Research Assistant¶

Agent that researches topics using web search and synthesizes information.

Skills: Tool use, ReAct, synthesis

2. Code Reviewer¶

Autonomous PR review with suggestions and tests.

Skills: GitHub integration, code analysis, feedback

3. Data Analyst Agent¶

Query databases, analyze data, create visualizations.

Skills: SQL, Python execution, reasoning

4. Customer Support Agent¶

Multi-turn conversations with CRM integration.

Skills: Memory, tool use, escalation

5. Workflow Automator¶

Complex multi-step business workflows.

Skills: Multi-agent, orchestration, error handling

📈 Evaluation Metrics¶

Task Success¶

Task completion rate: % of tasks finished correctly
Steps to completion: Efficiency measure
Tool use accuracy: Correct tool selection
Error recovery: Handling failures

Quality Metrics¶

Answer accuracy: Correctness of final output
Reasoning quality: Logical coherence
Tool call precision: Appropriate tool use
Response relevance: On-topic outputs

Production Metrics¶

Cost per task: Token usage + API calls
Latency: Time to completion
Reliability: Success rate over time
Safety: Harmful action rate

💡 Best Practices¶

DO ✅¶

Start with single-agent before multi-agent
Provide clear tool descriptions
Add error handling and retries
Set maximum iterations (prevent loops)
Log all agent actions
Test with edge cases
Monitor costs closely
Add human approval for critical actions

DON’T ❌¶

Give agents unrestricted access
Skip input validation
Ignore infinite loops
Forget rate limits
Trust agent outputs blindly
Skip safety checks
Ignore error logs

Agent Design Principles¶

Clear Role: Define specific purpose
Limited Tools: Only necessary capabilities
Good Prompts: Detailed instructions
Error Handling: Graceful failures
Monitoring: Track all actions
Safety: Approve critical actions
Testing: Comprehensive scenarios

🔗 Resources¶

Courses¶

Documentation¶

Papers¶

Communities¶

LangChain Discord
AutoGen GitHub
r/LangChain

🎓 What’s Next?¶

Real-World Applications →

Business process automation
Research and analysis
Code generation and review
Customer service
Data processing pipelines

Advanced Topics →

Agent fine-tuning
Custom tool development
Distributed agent systems
Agent-to-agent communication

Ready to build autonomous agents? → Start with 00_START_HERE.ipynb

Questions? → Check the projects/ folder for complete examples

🤖 Remember: Great agents combine reasoning with reliable tools!

Start here: 00_START_HERE.ipynb